Systems and methods are disclosed for asset health assessment and fleet management. An example method may include classifying a first power system asset into a first sub-system and a second sub-system. The example method may also include measuring, by a processor of a protection relay and from a first power system asset, electrical, thermal, and/or mechanical data associated with the first power system asset. The example method may also include identifying a first fault feature for the first sub-system, wherein the first fault feature is influenced by load oscillations in the first power system asset. The example method may also include comparing the first fault feature to a second fault feature of a third sub-system in a second power system asset, wherein the second fault feature is the same as the first fault feature, and wherein the second fault feature is not associated with load oscillations. The example method may also include adjusting a threshold value based on the comparison of the first fault feature to the second fault feature of the third sub-system. The example method may also include calculating, by the processor and for a first sub-system of the first power system asset, a first value based on the electrical, thermal, and/or mechanical data. The example method may also include calculating, by the processor, based on the first value, and using recent measurement data, a second value associated with the first sub-system. The example method may also include calculating, by the processor and using historical average data, a third value associated with the first sub-system. The example method may also include determining, by the processor and based on the second value and the third value, a fourth value associated with the first power system asset. The example method may also include determining, by the processor, that the fourth value is greater than a threshold value. The example method may also include generating, by the processor, a warning based on the determination that the fourth value is greater than the threshold value.

System and methods for monitoring and control infrastructure equipment uses extended reality playback to monitor such infrastructure equipment. The system and method advantageously blends videos and images of physical equipment with computer-generated representations of events to enable playback of the events, including any user interactions with the equipment. The extended reality playback presents events captured by real-time event logs for the infrastructure equipment using extended reality media content. The playback is composed of a real-time sequence of media content depicting one or more equipment operations corresponding to the events as a continuous presentation. The real-time sequence can then be displayed in real time and stored for subsequent playback at any time by a user. The real-time sequence can also be compared to a verified “golden” sequence to detect errors in the infrastructure equipment.

Methods, mediums, and systems include use of a system manger application in a data processing system for fault detection a building automation system using deep learning, to receive point data for a hardware being analyzed, where the received point data is contaminated data, train a deep learning model for the hardware being analyzed, generate predicted data based on the deep learning model, analyze the predicted data and the received point data, identify a fault in the hardware being analyzed according to the received point data and the predicted data, and produce a fault report according to the identified fault.

A management apparatus monitors and/or controls a facility device. The management apparatus includes a setting unit, a storage unit, a determination unit, and an extraction unit. The setting unit sets association information associating identification information in order to specify a register used by the facility device with a data point regarding monitoring and/or control of the facility device. The storage unit stores history information that is association information previously set by the setting unit. The determination unit determines the use state of the register in the facility device. The extraction unit extracts the history information identical or similar to the use state of the register determined by the determination unit from the history information.

A method for controlling a technical system via a control system, wherein the control system, after processing a request from an operator, generates a response message to the request such that if a faulty state of the technical system occurs, then associated fault messages are linked in an automated manner to the request and the response message in the time between the request and the generation of the response message, and a corresponding item of information relating thereto is presented to the operator, where the link between the request, response message and fault messages is provided with a digital signature of the operator who made the request to the control system.

A system for analyzing operational data associated with a plant that has processing equipment configured and controlled to run a process involving at least one tangible material. Actual operational data corresponding to plant operations is received by a computing device that may relate to production, equipment, workforce, automation systems, safety, and/or cybersecurity of the plant. A model of the plant is generated based on the actual operational data, where the model indicates ideal plant operations including model operational data. The actual plant operational data and the model operational data are compared. Based on the operational data and the comparison of the operational data to model operational data, at least one recommendation for an action associated with the plant is determined.

A method for performing a safe guard detection of unexpected operations launched by an operator for a manufacturing execution system (MED system) is based on a first database containing a set of operations, a set of operators, calendar information for a shift and calendar information for the equipment of the MES-system. The MES-systems further has a second database containing a login history of carried out logins of the operator. The detection of a malicious operation is carried out as to whether the operation complies with a set of rules defining allowed operations or with a learning module, in which specific roles of operators are contained and whether an operation complies with a specific role. In case of non-compliance, the operation is stored as an entry in an event trace file for generating alerts.

A service is stored in a non-volatile memory of a computing device and comprises instructions executable by a processor of the computing device. The processor generates an operational instance of the service, which comprises a reference to the service. The processor stores the operational instance of the service in the non-volatile memory with a read-write access right. The processor launches an executable instance of the service associated to the operational instance of the service. The launching comprises copying the instructions of the service from the non-volatile memory to a volatile memory of the computing device. The launching further comprises executing the instructions of the service copied into the volatile memory. The processor adds data generated by the execution of the instructions of the service to the operational instance of the service for permanent storage in the non-volatile memory.

An Animal Feed Mixer (Dairy or Beef) Scale indicator system records Feed Mixer Operating Speed along with the operating temperatures and hydraulic & lube oil pressures of the mixer gearboxes and hydraulic drive system and notifies the operator and records the temperature or pressures, along with the weight, date, and time, when limits have been exceeded.

The present invention relates to the field of intelligent machining, in particular to a parallel control method based on multi-period differential sampling and digital twinning technologies, the method comprising the following steps of: a. detecting machining conditions of dotting machine equipment by using a multi-period differential sampling technology; b. establishing a digital twinning control model; and c. controlling a simulation model of the dotting machine equipment according to a detection judgment result so as to perform parallel control on the dotting machine equipment. According to the parallel control method based on multi-period differential sampling and digital twinning modelling provided by the present invention, for the digital twinning model of the dotting machine equipment, the parallel control method establishes a simulation model and a detection model of the dotting machine equipment by using a virtual-real synchronization technology; simulation dotting machine equipment operates in synchronization with the physical dotting machine equipment.